Executive Human Resources Leader specializing in total rewards, M&A integration, and HR analytics
Bradenton, FL38y exp
SecureworksBoston University
“Operations/HR leader brought into an early-stage company to run a business unit and build HR from zero; drove 40% revenue growth and helped position the company for a buyout offer. Known for pragmatic process redesign (removing approval friction, centralizing contracts) and scaling people operations with tools like Pave and UKG, plus hands-on founder coaching using pilots and Theory of Constraints.”
Executive technology leader specializing in AI, blockchain, robotics, and healthcare
Boston, MA18y exp
Circular ProtocolHarvard Medical School
“Serial entrepreneur who has created several companies, brought them to market, raised as much as $15M, and achieved exits before moving on to new ideas. Combines an academic and institutional background spanning Harvard, MIT, Mass General Brigham, and the Department of War with two decades of consulting experience, and is especially motivated by building impactful technology.”
Principal Product Leader specializing in healthcare AI and data platforms
San Francisco Bay Area, CA29y exp
MedAdvisor SolutionsIndian Institute of Technology
“Healthcare product leader with deep oncology and pharma experience who has built both foundational clinical data platforms and AI-powered patient engagement workflows. Particularly notable for combining rigorous data standardization, LLM evaluation/guardrail design, and human-centered communication changes that drove measurable medication refill and adherence improvements. Also teaches AI to product managers and business analysts, bringing a pragmatic, education-first view of human-centered AI adoption.”
Mid-level Audio Research Scientist specializing in perceptual audio and ML
Brooklyn, NY8y exp
New York UniversityNYU
“Research-oriented candidate with internship experience at Apple and multiple audio/ML projects spanning speech processing evaluation, listener studies, CLAP-based audio workflows, and music prediction. They stand out for combining experimental design, statistical analysis, and applied machine learning in ambiguous research settings, including building a new onset-detection dataset and presenting VoiceFX work at workshops.”